As more devices find their way into the car to address people's desire for communication, entertainment and telematics services, a need may arise to ensure that these in-car devices can be operated without increasing the risk of traffic accidents. In this regard, if driving is considered a “eyes busy, hands busy” activity, then spoken language may be an appropriate choice for interaction between drivers and certain in-car devices. However, drivers may not be expected to learn and remember a complicated sets of spoken commands, and so rather than rely on a device-specific set of keywords, drivers may prefer to interact with in-car devices in a more flexible manner using natural language. Drivers may also prefer that their interaction with in-car devices be more personalized as well.
The in-car devices requiring interaction with the driver may include, for example, navigation systems. In the automotive industry, commercially available navigation systems may offer general-purpose turn-by-turn instructions, which may be helpful on certain occasions, but may be undesirable sometimes because they do not take into account a driver's knowledge, such as, for example, the driver's knowledge regarding certain roads or routes. The commercially available systems may also not take into account the external driving situation.
Existing route description generation systems may produce generic or at best navigation information that only accounts for limited driver specific characteristics. K. Höök, in her 1991 master's thesis entitled “An Approach to Route Guidance Interface” for the Department of Computer and Systems Sciences of Stockholm University, surveys the literature on driver modeling and concludes that an explicit user model is needed. However, K. Höök merely distinguishes users as tourist, resident and commuter navigators, in which routes are chunked and presented in different ways to these user groups. T. Pattabhiraman and N. Cercone in their paper entitled “Selection: Salience, relevance and the coupling between domain-level tasks and text planning”, which appeared in the 1990 Proceedings of the 5th International Workshop on NLG held in Pennsylvania, discuss the importance of salience and relevance in the content selection stage of route description generation. However, the notion of relevance discussed therein is pertinent to the communicative goals of the generator rather than those of the user. L. Fraczak et al., in their paper entitled “Automatic generation of subway directions: Salience graduation as a factor for determining message and form”, which appeared in the 1998 Proceedings of International Conference on Natural Language Generation, in Niagra-on-the-Lake, Canada, distinguish between known and unknown information, and discuss that the problem concerning the known information is to determine whether or not to make it explicit, but only deal with unknown information. Dale et al., in their paper entitled “Coral: Using natural language generation for navigational assistance”, which appeared in the 2003 Proceedings of the 26th Australian Computer Science Conference in Adelaide, Australia, discuss a desire to produce natural route descriptions using general purpose generation techniques but only feature a generation architecture, which emphasizes the micro-planning level to reproduce human writing patterns. In particular, the system described by Dale et al. takes Geographic Information Service (GIS) data input, segments it into hierarchical structures, and then uses aggregation and referring expression generation techniques to merge the data into coherent multi-clausal sentences.
Zukerman and Albrecht in the section entitled “Predictive statistical models for user modeling” of the 2001 publication “User Modeling and User-Adaptive Interaction” discuss two main user modeling techniques to adapt the behavior of systems in the contexts of a user-specific web page recommendation, namely, content-based modeling and collaborative modeling. Content-based modeling assumes that users exhibit particular behaviors under given circumstances, and that this behavior is repeated under similar circumstances, which may be most useful when a user's past behavior is a reliable indicator of future behavior. Collaborative modeling assumes that people with related characteristics tend to behave similarly under the same circumstances, which may be most useful when a user's behavior is similar to that of other (like-minded) users.
Existing generation components of dialogue systems have previously used collaborative modeling to predict a user behavior based on those exhibited by the group the user belongs to. Zukerman and Litman, identify a number of user features often considered in the content planning module of existing natural language generation (NLG) systems, including expertise or interests, preferences, user prototypes, and emotional state.
Although data may be collected from a large number of people, variations between individuals may reduce the predictability of the collaborative approach. The content-based approach, by contrast, may be more accurate for modeling individual user behaviors, but it use may be limited by a lack of sufficient training data. Accordingly, the two approaches may be combined to achieve better predictions. For example, the collaborative modeling may be used for a new user when there is not enough evidence to support a reliable prediction, and as more evidence is collected, the system may switch to the content-based approach.